Interpretable Deep Learning Prediction Model for Compressive Strength of Concrete

被引：0

作者：

Zhang, Wei-Qi ^{[1
]}

Wang, Hui-Ming ^{[1
]}

机构：

[1] College of Civil Engineering and Architecture, Xinjiang University, Urumqi

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2024年 / 45卷 / 05期

关键词：

compressive strength; concrete; deep learning; interpretation; SHAP method;

D O I：

10.12068/j.issn.1005-3026.2024.05.017

中图分类号：

学科分类号：

摘要：

To quickly and accurately predict the compressive strength of concrete，a prediction model is established using deep learning technology. The model is automatically optimized and adjusted using the Bayesian optimization algorithm，and the prediction results are analyzed by combining with the SHapley Additive exPlanations （SHAP） interpretable method， which overcomes the problem of the“black box”of the prediction model. The deep learning model is used to mine the potential law between each input feature parameter and compressive strength，the importance of the parameters on the compressive strength prediction results and the influence law is analyzed by visualizing the SHAP values of the input feature parameters. The results show that the constructed deep learning model outforms other traditional models. The SHAP analysis results are consistent with the experimental results，and the model better reflects the complex nonlinear relationship among the characteristic parameters，which can provide the basis and reference for the engineering design of concrete materials. © 2024 Northeast University. All rights reserved.

引用

页码：738 / 744and752

共 23 条

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